Manipulating Redox Kinetics using p‐n Heterojunction Biservice Matrix as both Cathode Sulfur Immobilizer and Anode Lithium Stabilizer for Practical Lithium–Sulfur Batteries
Bibliographic record
Abstract
Abstract As an attractive high‐energy‐density technology, the practical application of lithium–sulfur (Li–S) batteries is severely limited by the notorious dissolution and shuttle effect of lithium polysulfides (LiPS), resulting in sluggish reaction kinetics and uncontrollable dendritic Li growth. Herein, a p‐n typed heterostructure consisting of n ‐type MoS 2 nanoflowers embedded with p ‐type NiO nanoparticles is designed on carbon nanofibers (denoted as NiO‐MoS 2 @CNFs) as both cathode sulfur immobilizer and anode Li stabilizer for practical Li–S batteries. Such p‐n typed heterostructure is proposed to establish the built‐in electric field across the heterointerface for facilitated the positive charge to reach the surface of NiO‐MoS 2 , meanwhile inherits the excellent LiPS adsorption ability of p‐type NiO nanoparticles and catalytic ability of n‐type MoS 2 . As the anode matrix, the implementation of NiO–MoS 2 heterostructure can prevent the growth of Li dendrites by enhancing the lithiophilicity and reducing local current density. The obtained Li–S full battery exhibits an ultra‐high areal capacity over 7.3 mAh cm −2 , far exceeding that of current commercial Li‐ion batteries. Meanwhile, a stable cycling performance can be achieved under low electrolyte/sulfur ratio of 5.8 µL mg −1 and negative/positive capacity ratio of 1. The corresponding pouch cell maintains high energy density of 305 Wh kg −1 and stable cycling performance under various bending angles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".